KyungHee University
HULL FORM 최적설계와 데이터 마이닝
HULL FORM DESIGN OPTIMIZATION AND DATA
MINING
Shinkyu Jeong
Aerodynamic Design Exploration Lab.
Kyunghee Univ.
1
Contents
1
OBJECTIVE
2
DESIGN & OTIMIZATION
METHOD
3
RESULT
4
DATA MINING
5
CONCLUSION
OF SHAPE
2
OPTIMIZATION
Objective
To develop efficient hydrodynamic design
exploration tools for preliminary hull form design
Fast and accurate evaluation of objective
function
Kriging model
Design knowledge extraction
Analysis of Variance (ANOVA)
Self-Organizing Map (SOM)
3
RESPONSE SURFACE for OPTIMIZATION
Distribution of
function value
Conventional
Predictionsurface
Response
Objective
function
True value
Improvement
Probability for
improvement
Expected
Improvement (EI)
Design variable
Design Problem Definition
Objective Functions:
Minimize wave Drag at three design speed (FN=0.22,
0.27, 0.305)
Design Variables
4 design variable to control sectional area curve at Fore &
aft body variation
4 design variable to modify stem profile
8 design variable to modify local body profile
dv13
dv14
dv15
dv16
dv6
dv5
dv8
dv7
dv12
dv11
dv10
dv9
Overall design Procedure
Initial sample points
selection by
Latin Hypercube sampling
OBJ
Steady
Ship 1Flow
Construction
of Kriging Models
with
N sample points
60 points
Exploration of Pareto solutions
using MOGA
OBJ2
OBJ3
Clustering analysis (K-means)
to
select the promising points for design
N=N+m:
Sample points
Estimated
Population: 512
Generation: 100
Estimated
Estimated
Result of Shape Optimization (Pareto
Solutions)
Case 3
Case 3 Case 2
Obj1(Fn=0.22)
Case 2
Obj2 (Fn=0.27)
Obj3 (Fn=0.305)
Case 1
Obj3
Obj2
Case 1
Case 1
Case 3
Case 2
Obj1 (Fn=0,22)
Trade-off between obj1 & obj2, obj1&obj3
7
Performance Comparison
Compared with Series-60 Hull
Swet (%)
displacement (%)
Case-1
0.82
-0.04
Case-2
0.6
-0.27
Case-3
0.16
-0.19
CW (%)
8
Fn=0.22
Fn=0.27
Fn=0.305
Case-1
-70.9
-0.65
-14.62
Case-2
+23.20
-56.6
-56.25
Case-3
-43.17
-32.18
-37.26
Computational Cost
Present method
Sample points evaluation with SSF Code (90) : 15
mins
Kriging model construction and Optimization : 5 mins
Total computational time = 20 mins
Use SSF directly in MOGA
51200*3 evaluations : 18 days
9
10
ANOVA
extract the relation between objective functions and
design variables by decomposing the total variance
of model into the variance due to each design variable
quantitative information
Overall variance of model
Variance due to a variable only xi
The proportion of contribution from variable xi
to the global model
11
ANOVA -OBJ1(Low Speed)
InitialCase1
dv11:15%
dv4 :21%
dv7:7%
dv10: 39%
12 10
6
8
11
5
7
9
dv8:8%
dv13
dv14
dv15
dv16
dv6
dv5
12
dv8
dv7
dv12
dv11
dv10
dv9
dv4 is run angle
ANOVA - High Speed
InitialCase2
dv2:13%
dv12:22%
dv11:13%
dv5:6%
12 10
6
8
dv7:12%
11
5
7
9
dv8:24%
dv13
dv14
dv15
dv16
dv6
dv5
13
dv8
dv7
dv12
dv11
dv10
dv9
dv2 is entrance angle
Self-Organizing Map
(SOM)
Clustering methods using Neural network
Unsupervised, competitive learning
High-dimensional data → 2D map
Qualitative description of data
Each hexagon corresponding to each
design solution.
R={obj1, obj2, obj3, dv1, dv2,……}
Coloring the map by each objective
function value and design variable.
14
SOM -1024 solutions around pareto
front
Obj1
dv4
dv10
obj1:21%,
Obj2
Obj3
obj1:39
%,
dv11
obj1:15%,obj2:13
obj2:12%,
%
dv7
dv8
dv12
obj2:24%,
15
obj2:22
%,
Effect of Design Variables
High speed performance
dv4, dv7 and
dv12 is large and
dv8 is middle
Pareto Front
Low speed hull form
dv10 and
dv12 is small
dv4, dv7,dv8
and dv12 is
small
Low speed performance
16
12 10
6
8
11
5
7
9
High speed hull form
12
10
6
8
11
9
5
7
Decision Tree
A type of ANOVA using clustering method
Group A
dvi > ai
Group B
Find ai maximizing
17
Design rule for low speed
performance
If (dv11 > 0.049) and (dv11 > 0.0531) and
(dv8>0.0478),
hull form has a good low speed performance
18
Design rule for hign speed
performance
If (dv11 < 0.0482) and (dv4 < 0.00162) and
(dv7>0.0518),
hull form has a good high speed performance
19
Conclusion
An efficient hydrodynamic design exploration tools
for preliminary hull form design was developed
By using Kriging model, design span of
preliminary hull form was reduced to 20 minites to
16days
The useful design knowledge about hull form
was extracted
Analysis of Variance (ANOVA)
Self-Organizing Map (SOM)
Decision Tree (DT)
20
21
Total Drag and Wave Drag
Series-60 Hull (11,066 triangular panels on hull Surface)
George Mason University
NSWCCD
New Modification Function for SAC